Plot a heatmap of gradients of classes / loss function wtih respect to the input

Plot a heatmap showing the top positive and negative gene average gradients per cell type.

plotHeatmapGradsAgg(
  object,
  method = "class",
  top.n.genes = 15,
  scale.gradients = TRUE
)

Arguments

object

DigitalDLSorter object with a DigitalDLSorterDNN object containing gradients in the interpret.gradients slot.

method

Method to calculate gradients with respect to input features. It can be 'class' (gradients of predicted classes w.r.t. input features) or 'loss' (gradients of loss w.r.t. input features) ('class' by default).

top.n.genes

Top n genes (positive and negative) taken per cell type.

scale.gradients

Whether to calculate feature-wise z-scores of gradients (TRUE by default).

Value

A list of Heatmap-class objects, one for top positive and another one for top negative gradients.

See also

interGradientsDL trainDDLSModel

Examples

# \donttest{
set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(30, lambda = 5), nrow = 15, ncol = 10,
      dimnames = list(paste0("Gene", seq(15)), paste0("RHC", seq(10)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(10)),
    Cell_Type = sample(x = paste0("CellType", seq(2)), size = 10,
                       replace = TRUE)
  ),
  rowData = data.frame(
    Gene_ID = paste0("Gene", seq(15))
  )
)
DDLS <- createDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  sc.filt.genes.cluster = FALSE
)
#> === Bulk RNA-seq data not provided
#> === Processing single-cell data
#>       - Filtering features:
#>          - Selected features: 15
#>          - Discarded features: 0
#> 
#> === No mitochondrial genes were found by using ^mt- as regrex
#> 
#> === Final number of dimensions for further analyses: 15
prop.design <- data.frame(
  Cell_Type = paste0("CellType", seq(2)),
  from = c(1, 30),
  to = c(15, 70)
)
DDLS <- generateBulkCellMatrix(
  object = DDLS,
  cell.ID.column = "Cell_ID",
  cell.type.column = "Cell_Type",
  prob.design = prop.design, 
  num.bulk.samples = 50,
  verbose = TRUE
)
#> 
#> === The number of bulk RNA-Seq samples that will be generated is equal to 50
#> 
#> === Training set cells by type:
#>     - CellType1: 4
#>     - CellType2: 3
#> === Test set cells by type:
#>     - CellType1: 2
#>     - CellType2: 1
#> === Probability matrix for training data:
#>     - Bulk RNA-Seq samples: 38
#>     - Cell types: 2
#> === Probability matrix for test data:
#>     - Bulk RNA-Seq samples: 12
#>     - Cell types: 2
#> DONE
DDLS <- simBulkProfiles(DDLS)
#> === Setting parallel environment to 1 thread(s)
#> 
#> === Generating train bulk samples:
#> 
#> === Generating test bulk samples:
#> 
#> DONE
DDLS <- trainDDLSModel(
  object = DDLS,
  batch.size = 12,
  num.epochs = 5
)
#> === Training and test from stored data
#>     Using only simulated bulk samples
#>     Using only simulated bulk samples
#> Model: "DigitalDLSorter"
#> _____________________________________________________________________
#> Layer (type)                   Output Shape               Param #    
#> =====================================================================
#> Dense1 (Dense)                 (None, 200)                3200       
#> _____________________________________________________________________
#> BatchNormalization1 (BatchNorm (None, 200)                800        
#> _____________________________________________________________________
#> Activation1 (Activation)       (None, 200)                0          
#> _____________________________________________________________________
#> Dropout1 (Dropout)             (None, 200)                0          
#> _____________________________________________________________________
#> Dense2 (Dense)                 (None, 200)                40200      
#> _____________________________________________________________________
#> BatchNormalization2 (BatchNorm (None, 200)                800        
#> _____________________________________________________________________
#> Activation2 (Activation)       (None, 200)                0          
#> _____________________________________________________________________
#> Dropout2 (Dropout)             (None, 200)                0          
#> _____________________________________________________________________
#> Dense3 (Dense)                 (None, 2)                  402        
#> _____________________________________________________________________
#> BatchNormalization3 (BatchNorm (None, 2)                  8          
#> _____________________________________________________________________
#> ActivationSoftmax (Activation) (None, 2)                  0          
#> =====================================================================
#> Total params: 45,410
#> Trainable params: 44,606
#> Non-trainable params: 804
#> _____________________________________________________________________
#> 
#> === Training DNN with 38 samples:
#> 
#> === Evaluating DNN in test data (12 samples)
#>    - loss: NaN
#>    - accuracy: 0.1667
#>    - mean_absolute_error: NaN
#>    - categorical_accuracy: 0.1667
#> 
#> === Generating prediction results using test data
#> DONE
## calculating gradients
DDLS <- interGradientsDL(DDLS)
plotHeatmapGradsAgg(DDLS, top.n.genes = 2)
#> $Absolute

#> 
#> $Positive

#> 
#> $Negative

#> 
# }